# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import collections.abc
import math
from itertools import repeat
from typing import Optional

import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import paddle.nn.initializer as initializer
from paddle.distributed import fleet
from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
from paddle.nn.functional.flash_attention import flash_attention

from ppdiffusers.configuration_utils import ConfigMixin
from ppdiffusers.models.modeling_utils import ModelMixin

from .transformer_engine_utils import TransformerEngineHelper

def is_model_parrallel():
    """
    check whether the current training is model parallel or not.
    """
    if paddle.distributed.get_world_size() > 1 and hasattr(fleet.fleet, "_hcg"):
        hcg = fleet.get_hybrid_communicate_group()
        if hcg.get_model_parallel_world_size() > 1:
            return True
        else:
            return False
    else:
        return False


def _ntuple(n):
    def parse(x):
        if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
            return tuple(x)
        return tuple(repeat(x, n))

    return parse


to_2tuple = _ntuple(2)


def modulate(x, shift, scale):
    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)


class PatchEmbed(nn.Layer):
    """Image to Patch Embedding"""

    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, bias=True):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = num_patches
        self.proj = nn.Conv2D(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)

    def forward(self, x):
        B, C, H, W = x.shape
        assert (
            H == self.img_size[0] and W == self.img_size[1]
        ), f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."

        x = self.proj(x).flatten(2).transpose((0, 2, 1))
        return x


class Mlp(nn.Layer):
    """MLP as used in Vision Transformer, MLP-Mixer and related networks"""

    def __init__(
        self,
        in_features,
        hidden_features=None,
        out_features=None,
        act_layer=nn.GELU,
        norm_layer=None,
        bias=True,
        drop=0.0,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features

        if is_model_parrallel():
            self.fc1 = fleet.meta_parallel.ColumnParallelLinear(
                in_features,
                hidden_features,
                weight_attr=None,
                has_bias=bias,
                gather_output=True,
            )
            self.fc2 = fleet.meta_parallel.ColumnParallelLinear(
                hidden_features,
                out_features,
                weight_attr=None,
                has_bias=bias,
                gather_output=True,
            )
        else:
            self.fc1 = nn.Linear(in_features, hidden_features, bias_attr=bias)
            self.fc2 = nn.Linear(hidden_features, out_features, bias_attr=bias)

        self.act = act_layer()
        self.drop1 = nn.Dropout(drop)
        self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
        self.drop2 = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        with get_rng_state_tracker().rng_state("global_seed"):
            x = self.drop1(x)
        x = self.norm(x)
        x = self.fc2(x)
        with get_rng_state_tracker().rng_state("global_seed"):
            x = self.drop2(x)
        return x


class Attention(nn.Layer):
    def __init__(
        self,
        dim: int,
        num_heads: int = 8,
        qkv_bias: bool = False,
        fused_attn: bool = False,
        qk_norm: bool = False,
        attn_drop: float = 0.0,
        proj_drop: float = 0.0,
        norm_layer: nn.Layer = nn.LayerNorm,
    ) -> None:
        super().__init__()
        assert dim % num_heads == 0, "dim should be divisible by num_heads"
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.scale = self.head_dim**-0.5
        self.fused_attn = fused_attn

        if is_model_parrallel():
            self.qkv = fleet.meta_parallel.ColumnParallelLinear(
                dim,
                dim * 3,
                weight_attr=None,
                has_bias=qkv_bias,
                gather_output=False,
            )
        else:
            self.qkv = nn.Linear(dim, dim * 3, bias_attr=qkv_bias)
        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
        self.attn_drop = nn.Dropout(attn_drop)
        if is_model_parrallel():
            self.proj = fleet.meta_parallel.RowParallelLinear(
                dim,
                dim,
                weight_attr=None,
                has_bias=True,
                input_is_parallel=True,
            )
        else:
            self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
        B, N, C = x.shape
        dtype = x.dtype
        qkv = self.qkv(x).reshape([B, N, 3, self.num_heads, self.head_dim]).transpose([2, 0, 3, 1, 4])
        q, k, v = qkv[0], qkv[1], qkv[2]
        q, k = self.q_norm(q), self.k_norm(k)

        if dtype in [paddle.float16, paddle.bfloat16]:
            x, _ = flash_attention(
                q.transpose([0, 2, 1, 3]),
                k.transpose([0, 2, 1, 3]),
                v.transpose([0, 2, 1, 3]),
                dropout=self.attn_drop.p,
                return_softmax=False,
            )
            x = x.transpose([0, 2, 1, 3])
        else:
            if self.fused_attn:
                x = F.scaled_dot_product_attention_(
                    q.transpose([0, 2, 1, 3]),
                    k.transpose([0, 2, 1, 3]),
                    v.transpose([0, 2, 1, 3]),
                    dropout_p=self.attn_drop.p if self.training else 0.0,
                ).transpose([0, 2, 1, 3])
            else:
                q = q * self.scale
                attn = q @ k.transpose([0, 1, 3, 2])
                attn = F.softmax(attn, axis=-1)
                with get_rng_state_tracker().rng_state("global_seed"):
                    attn = self.attn_drop(attn)
                x = attn @ v

        x = x.transpose([0, 2, 1, 3]).reshape([B, N, C])
        x = self.proj(x)
        with get_rng_state_tracker().rng_state("global_seed"):
            x = self.proj_drop(x)
        return x

class MlpWithNVTEBackend(nn.Layer):
    """MLP as used in Vision Transformer, MLP-Mixer and related networks"""

    def __init__(
        self,
        in_features,
        hidden_features=None,
        out_features=None,
        act_layer=nn.GELU,
        norm_layer=None,
        bias=True,
        drop=0.0,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features

        te = TransformerEngineHelper.get_te()
        if is_model_parrallel():
            self.fc1 = te.Linear(in_features, hidden_features, bias_attr=bias, parallel_mode='column', gather_output=True)
            self.fc2 = te.Linear(hidden_features, out_features, bias_attr=bias, parallel_mode='column', gather_output=True)
        else:
            self.fc1 = te.Linear(in_features, hidden_features, bias_attr=bias)
            self.fc2 = te.Linear(hidden_features, out_features, bias_attr=bias)

        self.act = act_layer()
        self.drop1 = nn.Dropout(drop)
        self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
        self.drop2 = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        with get_rng_state_tracker().rng_state("global_seed"):
            x = self.drop1(x)
        x = self.norm(x)
        x = self.fc2(x)
        with get_rng_state_tracker().rng_state("global_seed"):
            x = self.drop2(x)
        return x



class ParallelTimestepEmbedder(nn.Layer):
    """
    Embeds scalar timesteps into vector representations.
    """

    def __init__(self, hidden_size, frequency_embedding_size=256):
        super().__init__()
        if is_model_parrallel():
            self.mlp = nn.Sequential(
                fleet.meta_parallel.ColumnParallelLinear(
                    frequency_embedding_size,
                    hidden_size,
                    weight_attr=None,
                    has_bias=True,
                    gather_output=False,
                ),
                nn.Silu(),
                fleet.meta_parallel.RowParallelLinear(
                    hidden_size,
                    hidden_size,
                    weight_attr=None,
                    has_bias=True,
                    input_is_parallel=True,
                ),
            )
        else:
            self.mlp = nn.Sequential(
                nn.Linear(frequency_embedding_size, hidden_size),
                nn.Silu(),
                nn.Linear(hidden_size, hidden_size),
            )
        self.frequency_embedding_size = frequency_embedding_size

    @staticmethod
    def timestep_embedding(t, dim, max_period=10000):
        """
        Create sinusoidal timestep embeddings.
        :param t: a 1-D Tensor of N indices, one per batch element.
                          These may be fractional.
        :param dim: the dimension of the output.
        :param max_period: controls the minimum frequency of the embeddings.
        :return: an (N, D) Tensor of positional embeddings.
        """
        half = dim // 2
        freqs = paddle.exp(x=-math.log(max_period) * paddle.arange(start=0, end=half, dtype="float32") / half)
        args = t[:, (None)].astype(dtype="float32") * freqs[None]
        embedding = paddle.concat(x=[paddle.cos(x=args), paddle.sin(x=args)], axis=-1)
        if dim % 2:
            embedding = paddle.concat(x=[embedding, paddle.zeros_like(x=embedding[:, :1])], axis=-1)
        return embedding

    def forward(self, t):
        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
        t_emb = self.mlp(t_freq.cast(self.mlp[0].weight.dtype))
        return t_emb


class ParallelLabelEmbedder(nn.Layer):
    """
    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
    """

    def __init__(self, num_classes, hidden_size, dropout_prob):
        super().__init__()
        use_cfg_embedding = dropout_prob > 0
        embedding_dim = num_classes + use_cfg_embedding
        # if is_model_parrallel():
        #     self.embedding_table = fleet.meta_parallel.VocabParallelEmbedding(embedding_dim, hidden_size)
        # else:
        # Paddle not support split with padding if not be divided in model parallel now
        self.embedding_table = nn.Embedding(embedding_dim, hidden_size)
        self.num_classes = num_classes
        self.dropout_prob = dropout_prob

    def token_drop(self, labels, force_drop_ids=None):
        """
        Drops labels to enable classifier-free guidance.
        """
        if force_drop_ids is None:
            drop_ids = (
                paddle.rand(
                    (labels.shape[0],),
                )
                < self.dropout_prob
            )
        else:
            drop_ids = paddle.to_tensor(force_drop_ids == 1)
        labels = paddle.where(drop_ids, self.num_classes, labels)
        return labels

    def forward(self, labels, force_drop_ids=None):
        use_dropout = self.dropout_prob > 0
        if (self.training and use_dropout) or (force_drop_ids is not None):
            with get_rng_state_tracker().rng_state("global_seed"):
                labels = self.token_drop(labels, force_drop_ids)
        embeddings = self.embedding_table(labels)
        return embeddings

class DiTBlockWithNVTEBackend(nn.Layer):
    """
    A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
    """

    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, fused_attn=False, use_fp8=False, **block_kwargs):
        super().__init__()
        te = TransformerEngineHelper.get_te()
        self.use_fp8 = use_fp8
        self.norm1 = te.LayerNorm(hidden_size, 1e-6, weight_attr=None, bias_attr=None)
        self.attn = te.MultiHeadAttention(hidden_size, num_heads, attention_dropout=0.0, bias_attr=True, attn_mask_type='padding')
        self.norm2 = te.LayerNorm(hidden_size, 1e-6, weight_attr=None, bias_attr=None)
        mlp_hidden_dim = int(hidden_size * mlp_ratio)
        approx_gelu = lambda: nn.GELU(approximate=True)  # 'tanh'
        self.mlp = MlpWithNVTEBackend(
            in_features=hidden_size,
            hidden_features=mlp_hidden_dim,
            act_layer=approx_gelu,
            drop=0,
        )
        if is_model_parrallel():
            self.adaLN_modulation = nn.Sequential(
                nn.Silu(),
                te.Linear(hidden_size, 6 * hidden_size, bias_attr=True, parallel_mode='column', gather_output=True)
            )
        else:
            self.adaLN_modulation = nn.Sequential(
                nn.Silu(),
                te.Linear(hidden_size, 6 * hidden_size, bias_attr=True),
            )

    def forward(self, x, c):
        with TransformerEngineHelper.fp8_autocast(
            self.use_fp8, TransformerEngineHelper.get_fp8_group()
        ):
            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, axis=1)
            x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa))
            x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
        return x

class DiTBlock(nn.Layer):
    """
    A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
    """

    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, fused_attn=False, **block_kwargs):
        super().__init__()
        self.norm1 = nn.LayerNorm(hidden_size, weight_attr=False, bias_attr=False, epsilon=1e-6)
        self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, fused_attn=fused_attn, **block_kwargs)
        self.norm2 = nn.LayerNorm(hidden_size, weight_attr=False, bias_attr=False, epsilon=1e-6)
        mlp_hidden_dim = int(hidden_size * mlp_ratio)
        approx_gelu = lambda: nn.GELU(approximate=True)  # 'tanh'
        self.mlp = Mlp(
            in_features=hidden_size,
            hidden_features=mlp_hidden_dim,
            act_layer=approx_gelu,
            drop=0,
        )

        if is_model_parrallel():
            self.adaLN_modulation = nn.Sequential(
                nn.Silu(),
                fleet.meta_parallel.ColumnParallelLinear(
                    hidden_size, 6 * hidden_size, weight_attr=None, has_bias=True, gather_output=True
                ),
            )
        else:
            self.adaLN_modulation = nn.Sequential(
                nn.Silu(),
                nn.Linear(hidden_size, 6 * hidden_size, bias_attr=True),
            )

    def forward(self, x, c):
        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, axis=1)
        x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa))
        x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
        return x


class ParallelFinalLayer(nn.Layer):
    def __init__(self, hidden_size, patch_size, out_channels):
        super().__init__()
        self.norm_final = nn.LayerNorm(hidden_size, weight_attr=False, bias_attr=False, epsilon=1e-06)
        if is_model_parrallel():
            self.linear = fleet.meta_parallel.ColumnParallelLinear(
                hidden_size,
                patch_size * patch_size * out_channels,
                weight_attr=None,
                has_bias=True,
                gather_output=True,
            )
            self.adaLN_modulation = nn.Sequential(
                nn.Silu(),
                fleet.meta_parallel.ColumnParallelLinear(
                    hidden_size, 2 * hidden_size, weight_attr=None, has_bias=True, gather_output=True
                ),
            )
        else:
            self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels)
            self.adaLN_modulation = nn.Sequential(nn.Silu(), nn.Linear(hidden_size, 2 * hidden_size))

    def forward(self, x, c):
        shift, scale = self.adaLN_modulation(c).chunk(2, axis=1)
        x = modulate(self.norm_final(x), shift, scale)
        x = self.linear(x)
        return x


class DiT(ModelMixin, ConfigMixin):
    """
    Diffusion model with a Transformer backbone.
    """

    _supports_gradient_checkpointing = True
    _use_memory_efficient_attention_xformers = True

    def __init__(
        self,
        sample_size: int = 32,  # image_size // 8
        patch_size: int = 2,
        in_channels: int = 4,
        out_channels: int = 8,
        num_layers: int = 28,
        num_attention_heads: int = 16,
        attention_head_dim: int = 72,
        mlp_ratio: float = 4.0,
        class_dropout_prob: float = 0.1,
        num_classes: int = 1000,
        learn_sigma: bool = True,
        transformer_engine_backend: bool = False,
        use_fp8: bool = False,
    ):
        super().__init__()
        self.sample_size = sample_size
        self.patch_size = patch_size
        self.in_channels = in_channels
        self.out_channels = in_channels * 2 if learn_sigma else in_channels
        hidden_size = num_attention_heads * attention_head_dim
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.num_attention_heads = num_attention_heads
        self.mlp_ratio = mlp_ratio
        self.class_dropout_prob = class_dropout_prob
        self.num_classes = num_classes
        self.learn_sigma = learn_sigma

        self.transformer_engine_backend = transformer_engine_backend
        self.use_fp8 = use_fp8
        self.gradient_checkpointing = False
        self.fused_attn = True

        # 1. Define input layers
        self.x_embedder = PatchEmbed(sample_size, patch_size, in_channels, hidden_size, bias=True)
        self.t_embedder = ParallelTimestepEmbedder(hidden_size)
        self.y_embedder = ParallelLabelEmbedder(num_classes, hidden_size, class_dropout_prob)
        num_patches = self.x_embedder.num_patches
        # Will use fixed sin-cos embedding:
        self.pos_embed = self.create_parameter(
            shape=(1, num_patches, hidden_size),
            default_initializer=initializer.Constant(0.0),
        )
        # self.add_parameter("pos_embed", self.pos_embed)

        # 2. Define transformers blocks
        if (transformer_engine_backend):
            self.blocks = nn.LayerList(
                [
                    DiTBlockWithNVTEBackend(hidden_size, num_attention_heads, mlp_ratio=mlp_ratio,
                                            fused_attn=self.fused_attn, use_fp8=use_fp8)
                    for _ in range(num_layers)
                ]
            )
        else:
            self.blocks = nn.LayerList(
                [
                    DiTBlock(hidden_size, num_attention_heads, mlp_ratio=mlp_ratio, fused_attn=self.fused_attn)
                    for _ in range(num_layers)
                ]
            )

        # 3. Define output layers
        self.final_layer = ParallelFinalLayer(hidden_size, patch_size, self.out_channels)

        self.initialize_weights()

    def initialize_weights(self):
        # Initialize transformer layers:
        def _basic_init(module):
            if TransformerEngineHelper.is_installed():
                TE_LINEAR = TransformerEngineHelper.get_te().Linear
            else:
                TE_LINEAR = type(None)
            if isinstance(
                module, (nn.Linear, fleet.meta_parallel.ColumnParallelLinear, fleet.meta_parallel.RowParallelLinear, TE_LINEAR)
            ):
                if isinstance(module, (TE_LINEAR)):
                    transpose_weight = paddle.transpose(module.weight, perm=[1,0])
                    initializer.XavierUniform()(transpose_weight)
                    module.weight.set_value(paddle.transpose(transpose_weight, perm=[1,0]))
                else:
                    initializer.XavierUniform()(module.weight)
                if module.bias is not None:
                    initializer.Constant(value=0)(module.bias)

        self.apply(_basic_init)

        # Initialize (and freeze) pos_embed by sin-cos embedding:
        pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.x_embedder.num_patches**0.5))
        self.pos_embed.set_value(paddle.to_tensor(pos_embed, dtype="float32").unsqueeze(0))

        # Initialize patch_embed like nn.Linear (instead of nn.Conv2D):
        w = self.x_embedder.proj.weight
        initializer.XavierUniform()(w.reshape([w.shape[0], -1]))
        initializer.Constant(value=0)(self.x_embedder.proj.bias)

        # Initialize label embedding table:
        initializer.Normal(std=0.02)(self.y_embedder.embedding_table.weight)

        # Initialize timestep embedding MLP:
        initializer.Normal(std=0.02)(self.t_embedder.mlp[0].weight)
        initializer.Normal(std=0.02)(self.t_embedder.mlp[2].weight)

        # Zero-out adaLN modulation layers in DiT blocks:
        for block in self.blocks:
            initializer.Constant(value=0)(block.adaLN_modulation[-1].weight)
            initializer.Constant(value=0)(block.adaLN_modulation[-1].bias)

        # Zero-out output layers:
        initializer.Constant(value=0)(self.final_layer.adaLN_modulation[-1].weight)
        initializer.Constant(value=0)(self.final_layer.adaLN_modulation[-1].bias)
        initializer.Constant(value=0)(self.final_layer.linear.weight)
        initializer.Constant(value=0)(self.final_layer.linear.bias)

    def enable_gradient_checkpointing(self, enable=True):
        self.gradient_checkpointing = enable

    def enable_xformers_memory_efficient_attention(self, attention_op: Optional[str] = None):
        self._use_memory_efficient_attention_xformers = True
        self.fused_attn = True

    def unpatchify(self, x):
        """
        x: (N, T, patch_size**2 * C)
        imgs: (N, H, W, C)
        """
        c = self.out_channels
        p = self.x_embedder.patch_size[0]
        h = w = int(x.shape[1] ** 0.5)
        assert h * w == x.shape[1]

        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
        x = paddle.einsum("nhwpqc->nchpwq", x)
        imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
        return imgs

    def forward(self, x, t, y):
        """
        Forward pass of DiT.
        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
        t: (N,) tensor of diffusion timesteps
        y: (N,) tensor of class labels
        """
        x = self.x_embedder(x) + self.pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
        t = self.t_embedder(t)  # (N, D)
        y = self.y_embedder(y)  # (N, D)
        c = t + y  # (N, D)

        recompute = (
            TransformerEngineHelper.get_te_recompute_func() if self.transformer_engine_backend
            else paddle.distributed.fleet.utils.recompute)

        for i, block in enumerate(self.blocks):
            if self.gradient_checkpointing:
                x = recompute(block, x, c, use_reentrant=False)
            else:
                x = block(x, c)  # (N, T, D)

        x = self.final_layer(x, c)  # (N, T, patch_size ** 2 * out_channels)
        x = self.unpatchify(x)  # (N, out_channels, H, W)
        return x


#################################################################################
#                   Sine/Cosine Positional Embedding Functions                  #
#################################################################################
# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py


def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
    """
    grid_size: int of the grid height and width
    return:
    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
    """
    grid_h = np.arange(grid_size, dtype=np.float32)
    grid_w = np.arange(grid_size, dtype=np.float32)
    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
    grid = np.stack(grid, axis=0)

    grid = grid.reshape([2, 1, grid_size, grid_size])
    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
    if cls_token and extra_tokens > 0:
        pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
    return pos_embed


def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
    assert embed_dim % 2 == 0

    # use half of dimensions to encode grid_h
    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)

    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
    return emb


def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
    """
    embed_dim: output dimension for each position
    pos: a list of positions to be encoded: size (M,)
    out: (M, D)
    """
    assert embed_dim % 2 == 0
    omega = np.arange(embed_dim // 2, dtype=np.float64)
    omega /= embed_dim / 2.0
    omega = 1.0 / 10000**omega  # (D/2,)

    pos = pos.reshape(-1)  # (M,)
    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product

    emb_sin = np.sin(out)  # (M, D/2)
    emb_cos = np.cos(out)  # (M, D/2)

    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
    return emb
